Ub-mesh: A Cost-efficient, Scalable Network Architecture For Large-scale Llm Training

As LLMs scale, their computational and bandwidth demands summation significantly, posing challenges for AI training infrastructure. Following scaling laws, LLMs amended comprehension, reasoning, and procreation by expanding parameters and datasets, necessitating robust computing systems. Large-scale AI clusters now require tens of thousands of GPUs aliases NPUs, arsenic seen successful LLAMA-3’s 16K GPU training setup, which took 54 days. With AI information centers deploying complete 100K GPUs, scalable infrastructure is essential. Additionally, interconnect bandwidth requirements surpass 3.2 Tbps per node, acold exceeding accepted CPU-based systems. The rising costs of symmetrical Clos web architectures make cost-effective solutions critical, alongside optimizing operational expenses specified arsenic power and maintenance. Moreover, precocious readiness is simply a cardinal concern, arsenic monolithic training clusters acquisition predominant hardware failures, demanding fault-tolerant web designs.

Addressing these challenges requires rethinking AI information halfway architecture. First, web topologies should align pinch LLM training’s system postulation patterns, which disagree from accepted workloads. Tensor parallelism, responsible for astir information transfers, operates wrong mini clusters, while information parallelism involves minimal but long-range communication. Second, computing and networking systems must beryllium co-optimized, ensuring effective parallelism strategies and assets distribution to debar congestion and underutilization. Lastly, AI clusters must characteristic self-healing mechanisms for responsibility tolerance, automatically rerouting postulation aliases activating backup NPUs erstwhile failures occur. These principles—localized web architectures, topology-aware computation, and self-healing systems—are basal for building efficient, resilient AI training infrastructures.

To heighten nan ratio of UB-Mesh successful large-scale AI training, we employment topology-aware strategies for optimizing corporate connection and parallelization. For AllReduce, a Multi-Ring algorithm minimizes congestion by efficiently mapping paths and utilizing idle links to heighten bandwidth. In all-to-all communication, a multi-path attack boosts information transmission rates, while hierarchical methods optimize bandwidth for broadcasting and trim operations. Additionally, nan study refines parallelization done a systematic search, prioritizing high-bandwidth configurations. Comparisons pinch Clos architecture uncover that UB-Mesh maintains competitory capacity while importantly reducing hardware costs, making it a cost-effective replacement for large-scale exemplary training.

In conclusion, nan UB IO controller incorporates a specialized co-processor, nan Collective Communication Unit (CCU), to optimize corporate connection tasks. The CCU manages information transfers, inter-NPU transmissions, and in-line information simplification utilizing an on-chip SRAM buffer, minimizing redundant representation copies and reducing HBM bandwidth consumption. It besides enhances computer-communication overlap. Additionally, UB-Mesh efficiently supports massive-expert MoE models by leveraging hierarchical all-to-all optimization and load/store-based information transfer. The study introduces UB-Mesh, an nD-FullMesh web architecture for LLM training, offering cost-efficient, high-performance networking pinch 95%+ linearity, 7.2% improved availability, and 2.04× amended costs ratio than Clos networks.

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